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miner_cmp_ab_pr_data.py
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miner_cmp_ab_pr_data.py
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#!/usr/bin/env python3
"""
Extract and cache the MPDS data of different types (i.e. harvesting approaches):
peer_reviewed experimental vs. in-house ab initio modeling
for the further comparison
"""
import os.path
import pickle
import math
from mpds_client import MPDSDataRetrieval, MPDSDataTypes
result_cache = 'mpds_cmp_ab_pr.pkl'
MILLIEV_TO_INVCM = 8.06554
INVMM_TO_INVCM = 10
MPDSDataRetrieval.chilouttime = 0.5
def sg_to_label(num):
if 195 <= num <= 230: return 'cub'
elif 168 <= num <= 194: return 'hex'
elif 143 <= num <= 167: return 'trig'
elif 75 <= num <= 142: return 'tet'
elif 16 <= num <= 74: return 'orth'
elif 3 <= num <= 15: return 'monocl'
elif 1 <= num <= 2: return 'tricl'
else: raise RuntimeError(f'Space group number {num} is invalid')
def short_formula(given_string, round_brackets_strip=True):
given_string = given_string.split()[0].strip().replace("x", "")
if given_string.startswith("[") and len(given_string.split("[")) == 2 and given_string.endswith("]"):
given_string = given_string.strip("[]")
if round_brackets_strip:
given_string = given_string.strip("()")
given_string = given_string.replace("(", "[").replace(")", "]")
return given_string
def is_scalar(value):
try: float(value)
except (TypeError, ValueError): return False
return True
def pr_prop_massage_1(deck):
if deck[4] != 'kJ g-at.-1':
return None
return deck
def ab_prop_massage_2(deck):
deck[3] = list(set(filter(None, deck[3]['modes_freqs']['0 0 0'])))
return deck
def pr_prop_massage_2(deck):
if deck[4] == 'eV':
deck[3] *= (1000 * MILLIEV_TO_INVCM)
elif deck[4] != 'mm-1':
deck[3] *= INVMM_TO_INVCM
return deck
def pr_prop_massage_3(deck):
if deck[4] != 'J K-1 g-at.-1':
return None
return deck
def pr_prop_massage_4(deck):
if deck[4] != 'GPa':
return None
return deck
def pr_prop_massage_5(deck):
if deck[4] != '':
return None
return deck
def ab_prop_massage_6(deck):
deck[3] = (deck[5], deck[3])
return deck
def pr_prop_massage_6(deck):
if deck[4] != '':
return None
deck[3] = (deck[5], deck[3])
return deck
phases_known_bg_type = set()
def bg_filter_1(deck):
if deck[4] != 'eV':
return None
return deck
def bg_filter_2(deck):
if deck[4] != 'eV':
return None
phases_known_bg_type.add(deck[2])
return deck
def bg_filter_3(deck):
if deck[2] in phases_known_bg_type:
return None
if deck[4] != 'eV':
return None
return deck
work_outline = {
'electrical conductivity': {
'meta': {
'interval': [math.exp(-28), math.exp(28)]
}
},
'Seebeck coefficient': {
'meta': {
'interval': [-1000, 1000]
}
},
'enthalpy of formation': {
'meta': {
'pr_prop_massage': pr_prop_massage_1,
'interval': [-900, 200]
}
},
'vibrational spectra': {
'meta': {
'pr_prop_name': 'phonons',
'ab_prop_conds': [
'sample.material.chemical_formula',
'sample.material.condition[0].scalar[0].value',
'sample.material.phase_id',
'sample.measurement[0].property.matrix'
],
'ab_prop_massage': ab_prop_massage_2,
'pr_prop_massage': pr_prop_massage_2,
'interval': [0, 2000]
}
},
'heat capacity at constant pressure': {
'meta': {
'pr_prop_massage': pr_prop_massage_3,
'interval': [0, 500]
}
},
'isothermal bulk modulus': {
'meta': {
'pr_prop_massage': pr_prop_massage_4,
'interval': [0.5, 2000]
}
},
'poisson ratio': {
'meta': {
'pr_prop_massage': pr_prop_massage_5,
'interval': [0, 1]
}
},
'effective charge': {
'meta': {
'ab_prop_conds': [
'sample.material.chemical_formula',
'sample.material.condition[0].scalar[0].value',
'sample.material.phase_id',
'sample.measurement[0].property.scalar',
'sample.measurement[0].property.units',
'sample.measurement[0].condition[1].refers_to'
],
'pr_prop_conds': [
'sample.material.chemical_formula',
'sample.material.condition[0].scalar[0].value',
'sample.material.phase_id',
'sample.measurement[0].property.scalar',
'sample.measurement[0].property.units',
'sample.measurement[0].condition[1].refers_to'
],
'ab_prop_massage': ab_prop_massage_6,
'pr_prop_massage': pr_prop_massage_6,
'interval': [-20, 20]
}
},
'energy gap for direct transition': {
'meta': {
'ab_prop_conds': ['sample.material.chemical_formula', 'sample.material.condition[0].scalar[0].value',
'sample.material.phase_id', 'sample.measurement[0].property.scalar', 'sample.measurement[0].property.units'],
'pr_prop_conds': ['sample.material.chemical_formula', 'sample.material.condition[0].scalar[0].value',
'sample.material.phase_id', 'sample.measurement[0].property.scalar', 'sample.measurement[0].property.units'],
'ab_prop_massage': bg_filter_1,
'pr_prop_massage': bg_filter_2,
'interval': [0.01, 20]
}
},
'energy gap for indirect transition': {
'meta': {
'ab_prop_conds': ['sample.material.chemical_formula', 'sample.material.condition[0].scalar[0].value',
'sample.material.phase_id', 'sample.measurement[0].property.scalar', 'sample.measurement[0].property.units'],
'pr_prop_conds': ['sample.material.chemical_formula', 'sample.material.condition[0].scalar[0].value',
'sample.material.phase_id', 'sample.measurement[0].property.scalar', 'sample.measurement[0].property.units'],
'ab_prop_massage': bg_filter_1,
'pr_prop_massage': bg_filter_2,
'interval': [0.01, 20]
}
},
'energy gap': {
'meta': {
'ab_prop_conds': ['sample.material.chemical_formula', 'sample.material.condition[0].scalar[0].value',
'sample.material.phase_id', 'sample.measurement[0].property.scalar', 'sample.measurement[0].property.units'],
'pr_prop_conds': ['sample.material.chemical_formula', 'sample.material.condition[0].scalar[0].value',
'sample.material.phase_id', 'sample.measurement[0].property.scalar', 'sample.measurement[0].property.units'],
'ab_prop_massage': bg_filter_3,
'pr_prop_massage': bg_filter_3,
'interval': [0.01, 20]
}
},
# 'magnetic moment': {} # TODO after new data 2022 release
# 'infrared spectra': {} # TODO after new data 2022 release
# 'Raman spectra': {} # TODO after new data 2022 release
}
phase_formulae = {}
def get_ab_pr_values(
ab_prop_name,
pr_prop_name=None,
interval=[0, 1],
ab_prop_conds=None,
pr_prop_conds=None,
ab_prop_massage=None,
pr_prop_massage=None
):
if not pr_prop_name:
pr_prop_name = ab_prop_name
ab_data, pr_data = {}, {}
print('#' * 50, 'downloading', ab_prop_name)
mpds_api = MPDSDataRetrieval(dtype=MPDSDataTypes.AB_INITIO)
for deck in mpds_api.get_data({'props': ab_prop_name}, fields={'P': ab_prop_conds or [
'sample.material.chemical_formula',
'sample.material.condition[0].scalar[0].value',
'sample.material.phase_id',
'sample.measurement[0].property.scalar'
]}):
if ab_prop_massage:
deck = ab_prop_massage(deck)
if not deck:
continue
if is_scalar(deck[3]) and not interval[0] < deck[3] < interval[1]:
print('Skipping value: %s' % str(deck))
continue
ab_data.setdefault(deck[2], []).append(deck[3])
phase_formulae[deck[2]] = (short_formula(deck[0]), sg_to_label(deck[1]))
print('#' * 50, 'downloading', pr_prop_name)
mpds_api = MPDSDataRetrieval(dtype=MPDSDataTypes.PEER_REVIEWED)
for deck in mpds_api.get_data({'props': pr_prop_name}, fields={'P': pr_prop_conds or [
'sample.material.chemical_formula',
'sample.material.condition[0].scalar[0].value',
'sample.material.phase_id',
'sample.measurement[0].property.scalar',
'sample.measurement[0].property.units',
'sample.measurement[0].condition[0].units',
'sample.measurement[0].condition[0].name',
'sample.measurement[0].condition[0].scalar'
]}):
if pr_prop_massage:
deck = pr_prop_massage(deck)
if not deck:
continue
if is_scalar(deck[3]) and not interval[0] < float(deck[3]) < interval[1]:
print('Skipping value: %s' % str(deck))
continue
pr_data.setdefault(deck[2], []).append(deck[3])
phase_formulae[deck[2]] = (short_formula(deck[0]), sg_to_label(deck[1]))
for phase_id in ab_data:
if phase_id not in pr_data:
continue
#print("%s: %s vs. %s" % (phase_formulae[phase_id], ab_data[phase_id], pr_data[phase_id]))
work_outline[ab_prop_name].setdefault('data', []).append(
(phase_formulae[phase_id], ab_data[phase_id], pr_data[phase_id])
)
if __name__ == "__main__":
if not os.path.exists(result_cache):
for key, value in work_outline.items():
get_ab_pr_values(
key,
pr_prop_name=value['meta'].get('pr_prop_name'),
interval=value['meta'].get('interval'),
ab_prop_conds=value['meta'].get('ab_prop_conds'),
pr_prop_conds=value['meta'].get('pr_prop_conds'),
ab_prop_massage=value['meta'].get('ab_prop_massage'),
pr_prop_massage=value['meta'].get('pr_prop_massage')
)
del work_outline[key]['meta'] # for pickling
# saving as a cache for future re-use
with open(result_cache, 'wb') as pickle_file:
pickle.dump(work_outline, pickle_file, protocol=2)
with open(result_cache, 'rb') as pickle_file:
work_outline = pickle.load(pickle_file)
for key, value in work_outline.items():
print('#' * 50, 'comparing', key)
for item in value['data']:
print(item)